Rotation and gray-scale transform invariant texture recognition using hidden Markov model
Rotation and gray-scale transform invariant texture recognition using hidden Markov model
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DOI：
10.1109/icassp.1992.226274
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发表时间：
1992-03
期刊：
影响因子：
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通讯作者：
J.-L. Chen;A. Kundu
中科院分区：
文献类型：
--
作者：
J.-L. Chen;A. Kundu
在该方案的第一阶段中，正交镜像滤波器（QMF）银行被用作小波变换的纹理图像分解成子带。然后从每个子带图像中提取灰度变换不变特征。在第二阶段中，子带序列被建模为隐马尔可夫模型（HMM），并为每一类纹理设计一个HMM。在识别过程中，未知纹理与所有模型进行匹配。最佳匹配模型识别纹理类别。实验中的旋转角度在-90度和90度之间随机选择。报告的分类准确率高达95%。
In the first stage of the proposed scheme the quadrature mirror filter (QMF) bank is used as the wavelet transform to decompose the texture image into subbands. Gray scale transform invariant features are then extracted from each subband image. In the second stage, the sequence of subbands is modeled as a hidden Markov model (HMM), and one HMM is designed for each class of textures. During recognition, the unknown texture is matched against all models. The best matched model identifies the texture class. The angles of rotation in the experiments are selected randomly in between -90 degrees and 90 degrees . Up to 95% classification accuracy is reported.>